Algorithmic Thinking and Problem Solving Notes

Course Overview

• Focus: Algorithmic thinking and problem-solving for B.Tech curriculum (2024 Batch).
• Goals: Learn problem-solving techniques and algorithm development, data representation, and programming in C.

What is Computer Science?

• Not solely about programming; it revolves around computational problem-solving.
• Core concept: An algorithm is a step-by-step procedure to solve specific problems.

Key Components of Problem Solving:

• Representation: Capture relevant aspects of the problem via abstraction.
• Algorithm: A method to systematically solve the problem based on the representation.

Understanding Algorithms:

• Defined as a finite set of precise, unambiguous steps leading to a solution.
• Algorithms must be generalizable (i.e., applicable to classes of problems).

Properties of Algorithms:

• Correctness: Should return desired outputs for all valid inputs.
• Finiteness: Must terminate after completing required operations.
• Non-ambiguity: Clear and definitive instructions.
• Efficiency: Performance measured by time or space usage.

Expressing Algorithms:

• Methods: Natural language, Flow charts, Pseudo code, and actual coded programs.
• Example flow chart symbols: Start/End (Oval), Process (Rectangle), Decision (Diamond).

Sample Algorithms:

• Finding area of a rectangle, calculating simple interest, checking even/odd, determining prime numbers, etc.
• Example of prime checking algorithm provided.

Practical Coding Example (Limak and Bob):

• Problem: Comparing weight gain of two bears given specific conditions.
• Steps: Read inputs (weights, fruit numbers), calculate yearly weight increases, and determine how many years until Limak outweighs Bob.
• Includes C programming code snippets for implementation.

Key Concepts in Flow Charts:

• Visual representation of the flow of algorithms via shapes that illustrate processes and decisions.

References:

• Included URL for further reading on algorithms and pseudocode.